 
Based on the Additive Feature Attribution Methods [1]}, Interpretation Model assumes the flow prediction of one cluster as the summation of other areas' contributions. Each time interpreting one cluster's prediction, Interpretation Model computes the correlation between the cluster's neighbor area and the prediction result. The larger the area's correlation value, the more positive the area contributes to the prediction result. For instance, an area with a large correlation pushes the model to give a high value during the prediction of the cluster being interpreted.
